Hello, hello! In our digital marketing and data analytics series, we are examining key concepts and exploring how digital marketers can benefit from them. In previous episodes, we looked at creating a data analytics mindset and using predictive analytics in digital marketing. Today, we will delve into a new concept that will be connected to previous editions, so you can read them here!

I will talk about data mining and machine learning concepts for actionable insights. If you've read previous episodes, I mentioned predictive analytics and which methods you can conduct. As you can see below, data mining is one of the methods of applying predictive analytics. We already knew why we should use predictive analytics in our digital marketing cases. So, let's learn how we can apply data mining and machine learning. I have read most of the blogs and resources, and I think some things got complicated and mixed in there. While writing this piece of paper, I read journal articles, data mining books, and RapidMiner's course materials. Of course, I used my master's course materials as well. I will try to keep my edition simple to unlock the power of data :)

1) What is Data Mining and How It Works?

We live in a world awash with data. Every day, vast amounts of information are generated through our online activities, financial transactions, social media, mobile devices, and more. Within all of this data lie invaluable insights and opportunities for improved decision-making. But how can we find useful nuggets among the overwhelming piles of raw data? This is where data mining comes in.

In predictive analytics, data is used to predict the future or ascertain the impact of one variable on another. It is common knowledge that data mining is an effective and adaptable data analysis technology. Data mining is the process of cleaning raw data and discovering patterns and knowledge from large sets of data. It utilises sophisticated statistical analysis, modelling, artificial intelligence, machine learning, and database technology to reveal valuable insights that would otherwise remain hidden. Data mining finds useful information and relationships buried within all that data, transforming it into knowledge that can be acted upon. The knowledge gleaned from data mining can be applied to optimise processes, identify opportunities, forecast outcomes, and give a competitive advantage.

🤖 Data mining and machine learning are like two sides of the same coin. Data mining employs techniques such as data cleaning, integration, and preprocessing as critical initial steps before analysis can occur. It focuses on preparing and exploring the data to discover patterns and relationships within it. Machine learning then utilises some of those patterns to build models that can make predictions and automated decisions. So, while data mining reveals insights descriptively from the data, machine learning leverages those insights predictively to train highly accurate models.

The processes work hand in hand - data mining provides the necessary data foundation for machine learning to then perform its modelling and algorithmic magic. Together, they are incredibly powerful in helping us analyse data and make smarter choices. So, in a way, data mining gifts machine learning with beautifully wrapped packages of clean, rich data to fuel its engines. The combination of the two gives us both descriptive and predictive power from our data.

2) CRISP-DM Framework for Data Mining

The CRISP-DM framework has been regarded as the most appropriate and inclusive guiding principle for executing analytics initiatives. CRISP-DM (CRoss-Industry Standard Process for Data Mining) methodology was founded in 1999 and outlines and guides the most frequently used procedures in data mining operations. The CRISP-DM methodology consists of six main stages, including business understanding, data understanding, data preparation, modelling, evaluation, and deployment.

1. Business Understanding - Identify the project objectives, requirements, and desired outcomes based on business needs.

2. Data Understanding - Explore, inspect, and get familiar with the available data relevant to the business goals.

3. Data Preparation - Clean, integrate, and format the data to create a final dataset for modelling. This is often the most time-consuming phase.

4. Modelling - Apply data mining techniques like machine learning algorithms to the prepared data to create predictive models that reveal patterns and insights.

5. Evaluation - Test the model results against the original business objectives and requirements. Seek feedback from stakeholders.

6. Deployment - Implement the validated model in the business for improved decision-making, monitoring its operation and maintenance.

CRISP-DM provides a flexible, iterative framework to guide the core steps in any data mining initiative. It is industry and tool-agnostic, and adaptable across use cases. The methodology emphasises the importance of understanding business needs first and then mining the data to create actionable insights that solve real problems.

3) Data Mining Tasks & Methods

Here's the summary of data mining tasks and methods from my course material. Whether your task is related to prediction or segmentation (1st column), you can use these methods and machine learning algorithms such as k-means or decision tree algorithms (2nd column). If your data is labelled, it means that it is supervised; if it is unlabelled, it means unsupervised learning (3rd column).

Let's look at data mining methods in detail but basic :) I will talk about some important of them.

3) a. Classification

Classification is a popular data mining technique that assigns categories or classes to data points based on their attributes and characteristics. It works by training machine learning algorithms on data with known classifications, allowing the model to learn the distinguishing features of each class. When new unclassified data is introduced, the trained model analyses its attributes to predict which class it belongs to.

👉 Some commonly used classification algorithms include decision trees, logistic regression, neural networks, support vector machines, and naive Bayes classifiers. These statistical and machine learning models can process both structured and unstructured data to uncover class designations and relationships within large datasets.

The goal of classification is to build models with high predictive accuracy that reliably categorise new observations. It has many applications, such as fraud detection, sentiment analysis, image recognition, and targeted marketing. Overall, classification is a powerful data mining technique for understanding data distributions and making predictions by automating the process of classifying data.

3) b. Regression

Regression is a data mining technique used to predict continuous numeric values based on the relationship between input and target variables. It works by fitting a mathematical model to the data that minimises the prediction error. This statistical model establishes correlations between multiple explanatory variables and a response variable to estimate future outcomes.

The strength of the correlation is quantified by a coefficient between -1 and 1. A value of -1 indicates a negative correlation, 0 means no correlation, and 1 is a positive correlation.

Some commonly used regression algorithms include linear regression, logistic regression, polynomial regression, and regression trees. Regression is applied in many predictive analytics tasks, such as sales forecasting, risk assessment, and algorithmic trading. It leverages historical data to make numeric predictions in a wide range of real-world scenarios.

3) c. Clustering

Clustering is an unsupervised learning technique that groups data points based on shared characteristics and patterns without any predefined classes. It seeks to discover natural structures within data by measuring similarities between data instances. Objects in the same cluster are more similar to each other than objects in other clusters.

💡 The key difference from classification is that clustering does not rely on pre-labelled training data. It explores the data structure on its own to identify relationships and inform future predictive models.

Common clustering algorithms include K-means, hierarchical clustering, density-based clustering, and X-means. These algorithms group data points into distinct clusters based on metrics like distance, connectivity, and density. Clustering can reveal insightful distributions in complex datasets and is often used for market segmentation, anomaly detection, recommender systems, and image analysis.

When working with multivariate data from surveys, for instance, cluster analysis might be helpful in market research. Market researchers can better understand the links between various groups and split customers into market categories by using cluster analysis.

4) Machine Learning Concept

Machine learning is a subset of artificial intelligence that enables computers to learn patterns from data without explicit programming. The algorithms iteratively improve their performance and make data-driven predictions by analysing training data sets. In contrast, data mining focuses more on exploratory analysis to uncover insights. However, data mining can supply the labelled data that machine learning algorithms leverage to train predictive models. So while data mining reveals descriptive relationships, machine learning uses these discoveries to optimise predictive capabilities.

✨ Computer Scientist and machine learning pioneer Tom Mitchell provides a modern definition: “A computer program is said to learn from experience E for some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.”

"Machine learning is the study of computer algorithms that allow computer programs to automatically improve through experience." (Tom Mitchell)

Machine learning employs statistical learning techniques like neural networks, decision trees, regression, clustering, and support vector machines. By recognising complex patterns, machine learning models can make increasingly accurate forecasts and classifications without human intervention. The algorithms continuously adapt to new data, allowing the models to evolve and improve over time.

In summary, machine learning brings data mining insights to life through adaptive predictive modelling and algorithmic decision-making. It automates analytical model building and enables computers to learn without explicit programming. Machine learning and data mining work together to unlock greater value from data.

5) How do Data Mining and Machine Learning Give Digital Marketers a Competitive Edge?

Data mining and machine learning enable marketers to gain deeper insights into customer behaviour and preferences. By analysing patterns in large datasets, marketers can better understand their target audiences and identify new opportunities. Banks have been successful in using machine learning for credit assessment, making them early users of data mining technologies. Data mining techniques like association, clustering, and segmentation enable more personalised and relevant messaging. Machine learning further empowers marketers to make data-driven predictions about customer lifetime value, churn risk, purchase history, and response rates.

• Market Basket Analysis 🛒

Data mining techniques like market basket analysis help retailers uncover insights from transaction data. By analysing which products are frequently purchased together in baskets, retailers can better understand customer behaviour. This information could inform store layouts, cross-promotions, and the bundling of related products.

Specifically, data mining uncovers insights such as which products tend to be purchased together or which customers are most loyal. Machine learning builds on these insights to create customer propensity models that predict behaviours. This enables hyper-targeted marketing campaigns and product recommendations. Additionally, machine learning algorithms can optimise and improve digital marketing efforts over time as they process more behavioural data.

• Retail 🛍️

Loyalty card data enables even deeper analysis of individual purchasing patterns over time. This highly valuable customer data allows personalised recommendations and targeted promotions to be delivered. For example, supermarkets can print personalised coupons to incentivise customers to purchase products they don't normally buy.

• Direct Marketing 📩

In direct marketing, data mining helps focus promotional campaigns on the most likely respondents. By analysing demographic data and response rates, marketers can build predictive models to identify prospective customers and avoid wasting resources mailing everyone. Data mining supports more cost-effective and successful direct marketing efforts.

Overall, leveraging data mining and machine learning allows digital marketers to precisely reach the right customers with the right message at the right time. Automated analytics and predictive modelling drive smarter decision-making. The competitive edge comes from turning raw data into actionable intelligence that boosts conversion rates, loyalty, and marketing ROI. However, data security and the responsible use of personal data remain crucial considerations.

Don't forget to subscribe to "All About Digital Marketing & Data Analytics" to get notified when new episodes drop!

🤍 Subscribe Now 👇 https://www.linkedin.com/newsletters/7147708212526411776/?displayConfirmation=true

Resources:

Witten, I.H., Frank, E., Hall, M.A. & ProQuest (Firm) 2011; Data mining: practical machine learning tools and techniques, 3rd;4;3;4th; Elsevier/Morgan Kaufmann, Amsterdam.

Jaggia, S., Kelly, A., Lertwachara, K. & Chen, L. 2020, "Applying the CRISP‐DM Framework for Teaching Business Analytics", Decision sciences journal of innovative education, vol. 18, no. 4, pp. 612-634.

Martinez-Plumed, F., Contreras-Ochando, L., Ferri, C., Hernandez-Orallo, J., Kull, M., Lachiche, N., Ramirez-Quintana, M.J. & Flach, P. 2021, "CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories", IEEE transactions on knowledge and data engineering, vol. 33, no. 8, pp. 3048-3061.

AWS Blog, What is Data Mining?

Rapidminer Altair Academy